关键词: 交集型歧义, 消歧规则, 统计语言模型, 评分函数, 全切分

Abstract: Overlapping ambiguity is one of the key problems in Chinese words segmentation.In this paper，a new hybrid strategy which integrates rule-based method and statistical-based method is presented for solving the overlapping ambiguity.Firstly，rule-set is constructed automatically through error-driven learning which will be used for some ambiguities detection and resolution.Secondly，a score function based on N-Gram language model is constructed.Lastly，a rule-based module and a statistical-based module will be combined for solving all ambiguities detected by FMM&BMM and the rule-set.The experiments show that this hybrid method is more suitable for ambiguities detection and possesses the advantages of both rule-based and statistical-based methods for overlapping ambiguities resolution in Chinese words segmentation.

Key words: overlapping ambiguity, disambiguation rules, statistical language model, score function, full segmentation